Method for optimizing revenue or profit of a gambling enterprise

ABSTRACT

A method optimizes revenue or profit for a gambling enterprise, such as a casino. The method employs a plurality of different gaming units, such as table games and electronic gaming devices, in the gambling enterprise. A plurality of counts is employed, one of the counts for each of the different gaming units. A plurality of decision functions is employed, with at least one of the decision functions for each of the different gaming units. Revenue or profit optimization is employed as an objective function. Optimal values for the counts for each of the different gaming units are determined from the decision functions and a plurality of constraints in order to optimize the objective function.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.10/767,377, filed Jan. 29, 2004, the entire contents of which are herebyincorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention pertains generally to methods for optimizing revenue orprofit of a gambling enterprise and, more particularly, to such methodsfor optimizing revenue or profit generated by gaming units on a casinofloor.

2. Background Information

Gambling enterprises, such as casinos, generate gaming revenue in manyways and from many sources. These can include, but are not limited to,the operation of table games, electronic gaming devices (EGDs) such asslot machines, video lottery terminals (VLTs), video poker machines,keno, bingo, pulltabs, race and sports wagers, as well as other forms ofgaming that occur on the casino floor. However, table games and EGDsproduce the largest percentage of gaming revenue and profit for mostcasinos and are areas of particular interest and scrutiny by casinomanagers.

Additionally, the size of a casino floor is generally constrained eitherby direct regulation, which restricts total space, or practical ormonetary considerations on the part of the operator. The amount of spaceavailable to a casino operator within which to generate revenue andprofit is therefore finite. Casino operators thus wish to strive tooptimize revenue and profit in gaming operations given this limitedavailable floor space.

Gambling enterprises use various types of casino management systems(CMSs) to provide information on activity generated from gamingactivities. A CMS gathers data on money wagered at gaming unitsthroughout the casino, with information available generally on a dailybasis, although some CMSs provide information of even greater detail(e.g., by hour; by shift).

Additionally, casinos utilize various financial reporting systems(FRSs). Information from the CMS is often used by the FRS in developingfinancial statements and creating financial reports on gamingdepartments. These statements and reports provide casino managers withinformation on the overall revenue performance (“win”) from gaming andother sources at the casino property, as well as costs associated withoperating these departments, since the FRS is also often the primaryrepository of information on expenses throughout the property.Information is often provided grouped by broad category (e.g., casino;hotel; food) as well as by sub-category or department (e.g., tablegames; EGDs; casino cage (e.g., an area on the casino floor wherefinancial transactions are completed to serve the needs of the patronson the gaming floor as well as to provide cash, coin, and chip resourcesto the gaming units in operation on the gaming floor; patrons may, forexample, cash checks, change currency for coin or redeem chips won at atable game at the casino cage); rooms; food outlet A; food outlet B).

Aside from preparing financial statements, data from activities on thegaming floor are also collected to assist the marketing department inperforming various analyses. It is standard practice in the industry toestablish systems for collecting and tracking customer activity atcasinos for use in customer recognition and marketing programs, the goalof which is to attempt to generate as much customer visitation aspossible. The presumption is that more customer visits will tend togenerate more gaming time and will increase gaming revenue. Thus, casinooperators have tended to focus their effects on determining ways toincrease visitation through loyalty programs or promotions to selectedhigher worth customers, as determined by a customer database analysis.See, for example, U.S. Pat. No. 6,003,013.

It is known to control the cost of playing an individual electronicgaming device (e.g., slot machine; video poker machine) by configuringgame speed, payback percentage, and game appearance. See U.S. Pat. No.6,254,483.

It is also known to allocate different games to various game machinesbased upon time periods, dates, type of players or the traffic line ofplayers, without the replacement of the game machines. See U.S. Pat. No.6,354,943.

It is further known to manage gaming tables in a gaming facility bydetermining the performance of dealers and by estimating the revenue foreach gaming table. See U.S. Pat. No. 6,446,864.

It is also known to employ a casino drawing/lottery game to attempt tomaximize gaming revenues by influencing which kind of games playersplay. See U.S. Pat. No. 5,129,652.

It is further known to increase revenues by offering a relatively moreattractive loss ratio, while incurring essentially the same fixed costsfor a gambling operation. See U.S. Pat. No. 6,500,066.

There is room for improvement in methods for optimizing revenue orprofit of a gambling enterprise.

SUMMARY OF THE INVENTION

These needs and others are met by the present invention, which providesa method for optimizing profit or revenue of a gambling enterprise.

It is believed that known prior attempts to increase revenue or toincrease profit from gambling enterprises, such as casinos, have notbeen optimal and have not employed techniques to optimize revenue orprofit by analyzing data on past gaming activity to determine demandfunctions of gaming units employed, and for the optimization of profitby also analyzing the cost structure in place to service the gamingunits employed, in order to determine through such analysis the optimalsolution set of gaming units to employ to reach optimal revenue orprofit.

It is believed that no prior attempt has been made to optimize revenuefrom gaming units on a casino floor by determining the keycharacteristics of the gaming units, defining these key characteristicsas decision variables, analyzing demand functions for each of thesedecision variables, determining reasonable constraints, and applyinglinear or non-linear programming techniques to optimize the revenue.

Furthermore, it is believed that no prior attempt has been made tooptimize profit from gaming units on a casino floor by determining theirkey characteristics, defining these key characteristics as decisionvariables, analyzing demand functions for each of these decisionvariables, analyzing fixed and variable cost functions for each of therelated casino departments, determining reasonable constraints, andapplying linear or non-linear programming techniques to optimize theprofit.

The present invention optimizes revenue or profit of a gamblingenterprise, such as a casino or casino floor, by, for example,determining the mix of gaming units, such as table games and electronicgaming devices, such as slot machines, on the casino floor. The presentinvention thus provides a method for optimizing revenue or profitgenerated from gaming units on the casino floor.

In accordance with one aspect of the invention, a method for optimizingrevenue or profit for a gambling enterprise comprises: employing aplurality of different gaming units in the gambling enterprise;employing a plurality of counts, one of the counts for each of thedifferent gaming units; employing a plurality of decision functions, atleast one of the decision functions for each of the different gamingunits; employing revenue or profit optimization as an objectivefunction; and determining optimal values for the counts for each of thedifferent gaming units from the decision functions in order to optimizethe objective function.

The method may include employing a casino floor having a physical areaas the gambling enterprise; employing a physical area associated witheach of the different gaming units; employing a plurality of differentelectronic gaming devices and a plurality of different table games assome of the different gaming units; and employing a constraint torequire the physical area of the different electronic gaming devices andthe physical area of the different table games to be less than or equalto the physical area of the casino floor when the optimal values aredetermined for the counts for each of the different gaming units.

The method may include employing a predetermined time period for atleast some of the decision functions; and including current andhistorical time series revenue data and current and historical timeseries cost data in the predetermined time period for the at least someof the decision functions.

The method may include employing revenue optimization as the objectivefunction; employing at least one constraint for at least some of thedifferent gaming units as one of the decision functions; determiningtime series revenue data for the different gaming units and determininga plurality of demand functions, one of the demand functions for each ofthe different gaming units; employing the demand functions as some ofthe decision functions; and determining the optimal values from the atleast one constraint and the demand functions.

The method may include employing profit optimization as the objectivefunction; employing at least one constraint for at least some of thedifferent gaming units as one of the decision functions; determiningtime series revenue data for the different gaming units and determininga demand function for each of the different gaming units; determiningtime series cost data for the different gaming units and determining acost function for the different gaming units; employing the demandfunction for each of the different gaming units and the cost functionfor the different gaming units as some of the decision functions; anddetermining the optimal values from the at least one constraint, thedemand function for each of the different gaming units and the costfunction for the different gaming units.

As another aspect of the invention, a method for optimizing revenue fora gambling enterprise comprises: identifying a plurality of differentclasses of gaming units in the gambling enterprise; employing aplurality of counts, one of the counts for each of the differentclasses; employing at least one decision function for the differentclasses; employing revenue optimization as an objective function;determining time series revenue data for the different classes anddetermining a plurality of demand functions, one of the demand functionsfor each of the different classes; and determining optimal values forthe counts for each of the different classes from the at least onedecision function and the demand functions, in order to optimize theobjective function and optimize revenue from the different classes ofgaming units.

The method may include employing a plurality of constraints associatedwith the different classes of gaming units; translating the constraintsto a plurality of mathematical expressions; employing the mathematicalexpressions as the at least one decision function; and employing thedemand functions and the mathematical expressions to determine theoptimal values for the counts.

The method may include determining whether the optimal values for thecounts are reasonable values; responsively adjusting the constraints;translating the adjusted constraints to a plurality of correspondingmathematical expressions; and re-determining optimal values for thecounts for each of the different classes from the demand functions andthe corresponding mathematical expressions, in order to optimize theobjective function and optimize revenue from the different classes ofgaming units.

The method may include employing at least one constraint associated withthe different classes of gaming units; translating the at least oneconstraint to at least one mathematical expression; employing the atleast one mathematical expression as the at least one decision function;translating the demand functions to a plurality of polynomial equations;and determining the optimal values from the at least one mathematicalexpression and the polynomial equations, in order to optimize theobjective function.

As another aspect of the invention, a method for optimizing profit for agambling enterprise comprises: identifying a plurality of differentclasses of gaming units in the gambling enterprise; employing a countfor each of the different classes; employing at least one decisionfunction for the different classes; employing profit optimization as anobjective function; determining time series revenue data for thedifferent classes and determining a demand function for each of thedifferent classes; determining time series cost data for the differentclasses and determining a cost function for the different classes; anddetermining optimal values for the counts for each of the differentclasses from the at least one decision function, the demand function foreach of the different classes and the cost function for the differentclasses, in order to optimize the objective function.

The method may include employing a plurality of constraints associatedwith the different classes of gaming units; translating the constraintsto mathematical expressions; employing the mathematical expressions asthe at least one decision function; and employing the demand functions,the cost functions and the mathematical expressions to determine theoptimal values for the counts.

The method may include identifying a plurality of gaming departmentsassociated with the plurality of different classes of gaming units;determining historical fixed costs and historical variable costs foreach of the gaming departments; and determining an historical fixed costfunction and an historical variable cost function for each of the gamingdepartments from the historical fixed costs and the historical variablecosts, respectively.

The method may include determining an historical fixed cost function andan historical variable cost function for each of the different classesof gaming units.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding of the invention can be gained from the followingdescription of the preferred embodiments when read in conjunction withthe accompanying drawings in which:

FIG. 1 is a block diagram of data gathering systems for a casinooperation and an optimization procedure in accordance with the presentinvention.

FIG. 2 is a flowchart of revenue or profit optimization steps inaccordance with the present invention.

FIG. 3 is a flowchart of revenue optimization steps in accordance withanother embodiment of the invention.

FIG. 4 is a flowchart of profit optimization steps in accordance withanother embodiment of the invention.

FIGS. 5A-5B are a flowchart of revenue and profit optimization steps inaccordance with another embodiment of the invention.

FIG. 6 is a block diagram of a personal computer as employed with theoptimization procedure of FIGS. 5A-5B.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

As employed herein, the term “gambling enterprise” shall expresslyinclude, but not be limited by, a casino, a casino floor, a slot parlor,a video lottery terminal (VLT) parlor, a “racino”, or any enterprisewhere patrons engage in gambling activity.

As employed herein, the term “table games” shall expressly include, butnot be limited by, table gambling games such as, for example, Blackjack(“21”), Craps (“Dice”), Roulette, Caribbean Stud Poker, Pai Gow, Pai GowPoker, Let it Ride, 3 Card Poker, Baccarat, Mini-Baccarat, and Sic Bo.

As employed herein, the term “electronic gaming device” or “EGD” shallexpressly include, but not be limited by, any coin-activated,currency-activated, debit-activated or credit-activated game on which aplayer may place a wager. Some non-limiting examples of EGDs includevideo poker; video blackjack; video keno; slot machines; video roulette;craps machines; EGDs by denomination (e.g., Penny; Nickel; Dime;Quarter; Half Dollar; Dollar; Five Dollar; Ten Dollar; Twenty-fivedollar; Hundred Dollar; Multi-denominational); EGDs by type (e.g., Reel;Video reel; Video poker; Proprietary or Participation; Progressive);EGDs by “personality” or theme associated with the game (e.g., DoubleDiamonds; Blazing 7s; Wheel of Fortune); and EGDs made by differentmanufacturers (e.g., Bally Gaming Systems; International GameTechnology; WMS Industries; Aristocrat; AC Coin & Slot; Alliance Gaming;Atronic; Mikohn Gaming; Shuffle Master Gaming).

As employed herein, the term “games” or “gaming” shall expresslyinclude, but not be limited by, table games, EGDs, Race and sports book,Poker, Keno, Bingo, and Pulltabs.

As employed herein, the term “denomination” is a specific monetaryamount of a coin or token. Some common denominations include, forexample, pennies, nickels, quarters and dollars, although largerdenominations are available as well.

As employed herein, the term “progressive” as applied to an EGD, such asa slot machine, provides a progressive slot machine, which takes apercentage of all coins that are played and adds it to a jackpot thatincreases in value. Players hitting the winning jackpot symbols win thetotal accumulated jackpot.

As employed herein, the term “proprietary” or “participation” gamesinclude slot machines or other games where the equipment supplierretains ownership of the machine and leases it to the casino, thus“participating” in the machine's revenue stream.

The present invention is described in association with optimizingrevenue or profit generated by gaming units on the casino floor. It willbe appreciated, however, that the present invention is applicable to awide range of gambling enterprises.

FIG. 1 shows data gathering systems 2,4 for a casino operation includinga casino floor 6. The casino floor 6 includes a plurality of gamingunits 8, which, in turn, include a plurality of table games 10, aplurality of electronic gaming devices (EGDs) 12 and other gamblinggames 14. As will be disclosed, data is generated from queries to thecasino management system (CMS) 2 to provide information on activitygenerated from gaming activities, and from the financial reportingsystem (FRS) 4. Typically, the FRS 4 is employed by the EGD department16 of the casino, which is responsible for the various EGDs 12, by thetable games department 18, which is responsible for the various tablesgames 10, and by the “Other Games” department 19, which is responsiblefor the various other gambling games 14. The FRS 4 provides both revenue(“win”) information 20 associated with casino revenue from the gamingunits 8, and expense information 22 associated with expense for thegaming units 8. In accordance with the present invention, anoptimization procedure 24 is disclosed to optimize the counts of thedifferent gaming units 8 from decision functions in order to optimize anobjective function, such as revenue or profit, for the casino. Althougha particular casino is disclosed, it will be appreciated that theinvention is applicable to a wide range of gambling enterprises, suchas, for example, another gambling enterprise or a casino having one ormore departments.

As shown in FIG. 2, the steps of the optimization procedure 24 areemployed to optimize revenue or profit for a gambling enterprise. First,at 26, a plurality of different gaming units, such as 8 of FIG. 1, areemployed in the gambling enterprise. Then, at 28, a plurality of countsare employed, with one of the counts for each of the different gamingunits. Next, at 30, a plurality of decision functions are employed, withat least one of the decision functions for each of the different gamingunits. At 32, revenue or profit optimization is employed as an objectivefunction. Finally, at 34, optimal values for the counts for each of thedifferent gaming units are determined from the decision functions inorder to optimize the objective function.

FIG. 3 shows another optimization procedure 40, which is employed tooptimize revenue for a gambling enterprise. First, at 42, a plurality ofdifferent classes of gaming units, such as 8 of FIG. 1, are employed inthe gambling enterprise. Next, at 44, a plurality of counts is employed,with one of the counts for each of the different classes. Then, at 46,at least one decision function, such as one or more constraints, isemployed for the different classes. At 48, revenue optimization isemployed as an objective function. Next, at 50, time series revenue datais determined for the different classes and a plurality of demandfunctions is determined, with one of the demand functions for each ofthe different classes. Finally, at 52, optimal values are determined forthe counts for each of the different classes from the at least onedecision function and the demand functions, in order to optimize theobjective function and optimize revenue from the different classes ofgaming units.

FIG. 4 shows another optimization procedure 60, which is employed tooptimize profit for a gambling enterprise. First, at 62, a plurality ofdifferent classes of gaming units, such as 8 of FIG. 1, are identifiedin the gambling enterprise. Next, at 64, a count is employed for each ofthe different classes. Then, at 66, at least one decision function, suchas one or more constraints, is employed for the different classes. At68, profit optimization is employed as an objective function. Next, at70, time series revenue data is determined for the different classes anda demand function is determined for each of the different classes. Then,at 72, time series cost data is determined for the different classes anda cost function is determined for the different classes. Finally, at 74,optimal values for the counts for each of the different classes aredetermined from the at least one decision function, the demand functionfor each of the different classes and the cost function for thedifferent classes, in order to optimize the objective function.

FIGS. 5A-5B show another optimization procedure 80, which is employed tooptimize revenue or profit for a gambling enterprise. First, at 82, theobjective function is defined. This determines the value that is to beoptimized, such as revenue generated by gaming units on the casinofloor, or profit generated from gaming units on the casino floor towhich casino departmental costs, both fixed and variable, are applied todetermine profitability. Next, at 84, the gaming unit properties(classes) are identified.

Example 1

For example, as was discussed above in connection with FIG. 1, thedifferent classes of the gaming units 8 on the casino floor 6 include aplurality of different classes of the table games 10 and a plurality ofdifferent classes of the EGDs 12.

Example 2

As another example, gaming units utilized in a casino operation togenerate revenue are grouped into classes based on relevantcharacteristics that distinguish them from other units. These classescould be, for example, game type for table games (e.g., blackjack;craps; roulette) and denomination of wager for EGDs (e.g., nickel;quarter; dollar).

Example 3

Within a table games operation, there are many types of games (e.g.,blackjack; craps; roulette; big six; pai gow; pai gow poker). Within anEGD operation, there are many different types of EGDs on the casinofloor. These EGDs can be classified, for example, in terms ofdenomination of wager (e.g., nickel; quarter; dollar), type (e.g., slot;video poker; progressive; participation), and “personality” (e.g.,Double Diamonds; Blazing 7s; Wheel of Fortune). A gambling enterprisedetermines which classification or characteristic of the gaming units onthe casino floor will be analyzed, in order to define the decisionvariables.

Next, at 86, a plurality of decision variables (DVs) associated withcasino revenue are defined.

Example 4

Casinos generate gaming revenue in many ways (e.g., the operation oftable games; electronic gaming devices (EGDs); keno; bingo; pulltabs;race and sportsbooks). Primary drivers of revenue and profit are, mosttypically, table games and EGD operations. Hence, DVs are typicallyassigned to the different classes of tables games and to the differentclasses of EGDs, although a wide range of other classes may be employed.

At 88, the analysis time period is determined.

Example 5

Preferably, about 36 months of data, if available, is gathered for thetime series revenue data, the demand functions and the time series costdata. Although an example analysis period is disclosed, it will beappreciated that a wide range of relatively shorter and relativelylonger analysis periods may be employed.

Even steps 90-96 determine a separate historical/current demand functionfor each of the different classes of gaming units (e.g., nickel EGD;quarter EGD; dollar EGD; blackjack; craps; roulette). Depending on thenature of the data series, the resultant best-fit equation (e.g., leastsquares; maximum r² value) may be, for example, linear (e.g., y=mx+b,wherein m is the slope and b is the y-intercept), polynomial (e.g.,y=b+c₁x+c₂x²+c₃x³+ . . . +c_(n)x^(n), wherein b and c₁, c₂, c₃ . . .c_(n) are constants), logarithmic (e.g., y=c ln x+b, wherein c and b areconstants and ln is the natural logarithm function), exponential (e.g.,y=ce^(bx), wherein c and b are constants and e is the base of thenatural logarithm), or power (e.g., y=cx^(b), wherein c and b areconstants). For example, a linear trend line is generally a best-fitwhen data values increase or decrease at a steady rate; a polynomialtrend line is generally a best-fit curve when data values fluctuate andthere are gains or losses over a large data set; a logarithmic trendline is generally a best-fit curve when the rate of change in the datavalues increases or decreases quickly and then levels out; anexponential trend line is generally a best-fit curve for data valuesthat rise or fall at increasingly higher rates; and a power trend lineis generally a best-fit curve when data values increase at a specificrate.

First, at 90, times series data for the decision variables areretrieved. Typically, this includes employing a predetermined analysisperiod, as was selected at 88, and a predetermined count of samples oftime series revenue data, including a revenue value and a count of thedifferent classes of gaming units for each of the samples. For example,using the CMS 2, the FRS 4 of FIG. 1 and/or other relevant data sources,the time series data is retrieved that reveals both total revenuegenerated (“win”) from a specific class of the gaming units 8, and thetotal number of units of that class on the casino floor 6 during thosesame periods of time. As determined at 96, the time series data of thistype is retrieved, at 90, for all of the different decision variables aswere determined at 86.

Due to the timeliness of data generally available from the FRS 4 of FIG.1 and other systems routinely used in casino operations, a demandfunction (or cost function, as is discussed below in connection withsteps 108 and 110) may incorporate both historical (e.g., one year ago;three years ago) and current (e.g., today; yesterday) demandcharacteristics in one equation or mathematical expression. If desired,separate long-term (e.g., greater than one year) and short-term (e.g.,less than one year) demand curves may be determined and employed.However, it is believed that relevancy issues suggest that demand curvespreferably aggregating the prior about three years of data (as selectedat step 88) will reveal the trends that are needed to properly solve forthe optimal state of the decision variables.

Next, at step 92, using suitable regression analysis techniques, ademand function for each of the decision variables is defined. With theregression analysis, each set of time series revenue data is analyzed todetermine its proper mathematical expression. These expressionseffectively represent the demand function for each of the classes ofgaming units utilized in the casino. For example, a statisticalregression analysis technique models the relationship between variables.Scatter plots may represent sample data points for differentcombinations of two variables (e.g., X and Y). A regression line may befit to the scatter plot to reveal a linear or a non-linear relationshipbetween the two variables. Also, a coefficient of determination, r², maybe employed as a measure of the strength of the regression relationship,in order to measure how well the regression line fits the data. Forexample, an r² value of about 0.9 may fit the data relatively well,while an r² value of about 0.75 would explain relatively less of thedata and, therefore, would not be as relatively good of a fit for thedata.

For example, the first pass through step 92 for a first decisionvariable may employ regression analysis to convert the time seriesrevenue data for a corresponding one of the classes of gaming units to afirst type of mathematical expression (e.g., a linear equation with anr² value of about 0.9) for this demand function. Then, the first passthrough step 94 would determine that there was no best fit, since onlyone type of mathematical expression had been considered, and step 92would be repeated.

Then, the second pass through step 92 for the first decision variablemay employ regression analysis to convert the time series revenue datafor the corresponding one of the classes of gaming units to a secondtype of mathematical expression (e.g., a polynomial equation with an r²value of about 0.75) for this demand function. Then, the second passthrough step 94 would determine that the linear equation, in thisexample, was the better fit of those two types of mathematicalexpressions.

Next, steps 92 and 94 may be repeated, as desired, for other types ofmathematical expressions (e.g., logarithmic; exponential; power). Hence,depending on the nature of the time series revenue data, the resultantbest-fit (e.g., maximum r² value) equation may be, for example, linear,polynomial, logarithmic, exponential or power. Then, the final passthrough step 94 for the first decision variable would select themathematical expression which provides a best fit (e.g., the maximum r²value) for the time series revenue data.

Then, at 96, it is determined if all of the demand functions have beendetermined for the various decision variables. In this example, sincethere are a plurality (e.g., six) of decision variables, even steps90-96 are repeated five times to determine the mathematical expressionsthat provide the best fit for the demand functions for all decisionvariables. After 96, the next step is 98.

Step 98 determines whether the objective function from step 82 is profitoptimization. If so, then the procedure resumes at 100. On the otherhand, if the selected objective function from step 82 is revenueoptimization, then the procedure resumes at 116.

If profit is determined to be the objective function to be optimized, at82 and 98, then even steps 100-114 determine historical/current costfunctions for each of the decision variables. At 100, the relevantgaming departments (e.g., table games; EGD) associated with thedifferent classes of gaming units are determined. Next, at 102,additional time series data for the selected analysis period of step 88is acquired from the FRS 4 of FIG. 1. For example, time series data oncosts within the relevant gaming departments are retrieved by line itemin order to be able to classify each line item as a fixed cost or avariable cost. In addition, the total number of gaming units served bythis gaming department during these same time periods is also retrieved.

Next, at 104, fixed costs and variable costs are segregated. Then, at106, the variable costs and the fixed costs within the various gamingdepartments are separately aggregated for each of those departments.

At 108, an historical fixed cost function (i.e., a constant) isdetermined for each department. As an example, there would be a fixedcost constant for the table games department and a fixed cost constantfor the electronic gaming devices department.

Similar to step 92, a variable cost function for each of the gamingdepartments is defined using suitable regression analysis techniques at110. For example, there would be a first variable cost function for thetable games department as a function of the count of table game units,and a second variable cost function for the electronic gaming devicesdepartment as a function of the count of electronic gaming devices. Forexample, a statistical regression analysis technique models therelationship between variables. Scatter plots may represent sample datapoints for different combinations of two variables (e.g., X and Y). Aregression line may be fit to the scatter plot to reveal a linear or anon-linear relationship between the two variables. Also, a coefficientof determination, r², may be employed as a measure of the strength ofthe regression relationship, in order to measure how well the regressionline fits the data. For example, an r² value of about 0.9 may fit thedata relatively well, while an r² value of about 0.75 would explainrelatively less of the data and, therefore, would not be as relativelygood of a fit for the data.

For example, the first pass through step 110 for the table gamesdepartment may employ regression analysis to convert the aggregatedhistorical variable costs to a first type of mathematical expression(e.g., a linear equation with an r² value of about 0.9) for thishistorical variable cost function. Then, the first pass through step 112would determine that there was no best fit, since only one type ofmathematical expression had been considered, and step 110 would berepeated.

Then, the second pass through step 110 for the table games departmentmay employ regression analysis to convert the aggregated historicalvariable costs to a second type of mathematical expression (e.g., apolynomial equation with an r² value of about 0.75) for this historicalvariable cost function. Then, the second pass through step 112 woulddetermine that the linear equation, in this example, was the better fitof those two types of mathematical expressions.

Next, steps 110 and 112 may be repeated, as desired, for other types ofmathematical expressions (e.g., logarithmic; exponential; power). Hence,depending on the nature of the variable cost data series, the resultantbest-fit (e.g., maximum r² value) equation may be, for example, linear,polynomial, logarithmic, exponential or power. Then, the final passthrough step 112 for the table games department would select themathematical expression which provides a best fit (e.g., the maximum r²value) for the historical variable costs.

Then, at 114, it is determined if all of the variable cost functionshave been determined for the various gaming departments. In thisexample, since there is also the EGD department, even steps 110-114 arerepeated to determine the mathematical expression that provides the bestfit for the historical variable costs for that department. After 114,the next step is 116.

At 116, one or more constraints associated with the different classes ofgaming units are determined. Examples of constraints are discussed belowin connection with Examples 6 and 11-14. For example, there existslimiting conditions that affect the operation of table games, EGDs, andother gaming units of a casino. For example, there is the finite size ofthe available space on the casino floor. In addition, management maydecide, for marketing or other reasons, that a minimal or maximum numberof a specific gaming unit type is needed. Next, at 118, one or moremathematical expressions are developed to represent the relevantconstraints on the overall operation of the casino floor.

Next, the optimization method is determined. Based on a review of all ofthe demand, cost, and constraints functions, at 120, either a linearprogramming application or a non-linear programming application isemployed to solve the objective function and, thus, to determine theoptimal values for the counts of gaming units of each class (e.g.,decision variable) that would optimize the objective function (i.e.,revenue; profit).

Next, at 122, it is determined which optimizing algorithm is appropriatebased on the resultant constraints and demand functions for revenueoptimization (or constraints, demand and cost functions for profitoptimization). If all decision functions and expressions are linear,then linear programming is applied at 124. Otherwise, if any one or moreof the decision functions or expressions is non-linear, then non-linearprogramming is applied at 134.

Linear programs, as applied at 124, are models that seek a solution toan objective function subject to certain limiting conditions orconstraints. In linear programs, all equations must be linear in nature(i.e., a power of 1). Linear programming, thus, employs linear functionsin which each variable appears in a separate term, there are no powersgreater than 1, and there are no logarithmic, exponential, ortrigonometric terms. For example, the expression y=mx+b is an example ofa linear function.

In contrast, non-linear programs, as applied at 134, are models thatseek a solution to an objective function subject to certain limitingconditions or constraints, although all equations are not required to benon-linear in nature. Non-linear programming, thus, employs one or morefunctions that are not linear. For example, the expression y=500x²+35x+6is non-linear, since x has a power of 2, which is greater than 1.

In the optimization analysis, at 124 or 134, mathematical expressionsthat represent all of the constraint and demand (or constraint, demandand cost) functions are input (e.g., into a spreadsheet 152 as shown inFIG. 6). Then, a linear programming application, at 124, or a non-linearprogramming application at 134 (e.g., applications 154,156 of FIG. 6),is applied to this data to determine the solution to the objectivefunction (i.e., the optimum count of each of the gaming units 8 of FIG.1 needed to optimize revenue or profit).

These values are reviewed, at 126 and 136, for reasonableness (e.g., asdiscussed in greater detail, below, in connection with step 126 ofExample 6) and, if necessary, the constraints are responsively adjusted(e.g., as discussed in greater detail, below, in connection with step128 of Example 7), at 128 and 138, respectively, until optimal andreasonable values of decision variables are obtained. Steps 136 and 138are essentially the same as steps 126 and 128, respectively. After step128 or 138, step 120 is repeated in order to re-determine the optimalvalues for the counts (at even steps 120-128 or even steps 120, 122 and134-138).

FIG. 6 shows a suitable processor, such as a personal computer (PC) 150,employed with the optimization procedure 80 of FIGS. 5A-5B. The variousdata are imported to the spreadsheet 152 (e.g., Excel marketed byMicrosoft Corporation of Redmond, Wash.), the demand functions (evensteps 90-96) or demand and cost functions (even steps 100-114) aredetermined, constraints (steps 116 and 118) presented as mathematicalexpressions are incorporated, and the linear or non-linear programmingapplications 154,156 (e.g., Large-Scale LP Solver Engine or Large-ScaleGRG Solver Engine marketed by Frontline Systems Inc. Incline Village,Nev.) are employed, although the invention is applicable to a wide rangeof methods for optimizing profit or revenue of a gambling enterprise,and a wide range of other suitable processors, operating systems,databases, interfaces, programming languages, spreadsheet applications,report applications, and linear and/or non-linear programmingapplications may be employed. The resultant optimal values of the gamingunit counts are then displayed on display 158. Preferably, these optimalvalues are employed, as shown with the optimization procedure 24 of FIG.1, to adjust the counts of the gaming units 8 on the casino floor 6 tocorrespond to the optimal values.

Linear programming and non-linear programming provide a variety ofdeterministic approaches used to solve complex computational problemswhen maximization or minimization of multiple values or decisionvariables (DVs) contained within a complex problem is desired. Thesegenerate results associated with multiple variables in an attempt tooptimize a particular value for a specific problem. The various linearand/or non-linear functions, which will reproduce the optimal value, arecollectively often referred to as the objective function.

Although linear and non-linear programming are disclosed, other suitablealgorithms may be employed to optimize decision variables associatedwith revenue or profit generated on the casino floor. For example,software algorithms may be employed including mathematical algorithmsusing, for example, quadratic techniques, matrix algebra and/orsimultaneous equation techniques.

Example 6

This example is directed to solving a selection problem of gaming unitson a casino floor. It will be appreciated that acquiring the best andmost productive gaming units for a casino is important to thecorresponding casino operator, since any incremental increase in patronvolume, generated by providing the most popular mix of gaming units topatrons, can have a significant impact on revenue and, thus,profitability. In addition, since casinos incur significant costs inacquiring table games and EGDs, it is important to spend capital dollarson the highest performing equipment, in order to generate a reasonablereturn.

This example selection problem is disclosed with respect to FIGS. 5A-5B.First, at 82, the objective function is defined as optimization ofprofitability generated from gaming units on the casino floor. Althoughone definition of the objective function is disclosed in this example,this objective function can be defined in other ways, such as, forexample, if generating the highest revenue, without regard for the costsinvolved in operating various gaming departments, is of concern, thenthe objective function would be defined as the optimization of revenuegenerated from the gaming units.

Next, at 84, the gaming units are classified in terms of the type ofgame they represent within the table games department (e.g., blackjack;craps; roulette). For EGDs, the gaming units on the casino floor areidentified by their denomination (or the minimum allowable wager). Theseidentifying properties are defined as the decision variables, at 86, andare shown, for this example, in Table 1:

TABLE 1 Notation Represents Decision Variable: Table Games (DV_(TG1))Blackjack Decision Variable: Table Games (DV_(TG2)) Craps DecisionVariable: Table Games (DV_(TG3)) Roulette Decision Variable: EGDs(DV_(EGD1)) Nickel EGD Decision Variable: EGDs (DV_(EGD2)) Quarter EGDDecision Variable: EGDs (DV_(EGD3)) Dollar EGD

Based on the availability of data, the analysis period in this example,as selected at 88, is 36 months. Then, time series revenue dataincluding total revenue generated and total units on the floor perperiod for 36 months is retrieved, at 90, from the FRS 4 of FIG. 1 forDV_(TG1) (i.e., all blackjack tables). Next, at 92, this data isanalyzed to determine, using regression analysis techniques for a linearequation, the corresponding mathematical expression that represents thedata most accurately. That expression, in this case, is determined to beEquation 1:

y=$12,180*DV _(TG1)  (Eq. 1)

wherein:y is monthly revenue generated from blackjack tables on the casino floor(DV_(TG1)).

Next, at 94, as was discussed in greater detail, above, in connectionwith FIGS. 5A-5B, by reviewing statistical properties (e.g., r²statistic) associated with this example linear equation and any otherequation types (not shown), it is determined that Equation 1 representsa “best fit” equation and, thus, represents the proper demand functionfor DV_(TG1).

Then, at 96, it is determined if all demand functions have beendetermined. Since there are six decision variables, in this example, theprocess is repeated, at 90, 92 and 94, until the demand function foreach of the six decision variables is determined. In this example, thesix demand functions, as determined, are shown in Table 2:

TABLE 2 Equation Decision Variable Demand Function 1 DV_(TG1) y =$12,180 * DV_(TG1) 2 DV_(TG2) y = $19,980 * DV_(TG2) 3 DV_(TG3) y =$14,340 * DV_(TG3) 4 DV_(EGD1) y = $2,460 * DV_(EGD1) 5 DV_(EGD2) y =$2,490 * DV_(EGD2) 6 DV_(EGD3) y = $930 * DV_(EGD3)

Although six linear equations are shown in Table 2, it will beappreciated that a wide range of counts and of different types ofequations may be employed, such as, without limitation, polynomial,logarithmic, exponential, or power. Although monthly revenue isdisclosed, smaller or larger time periods may be employed.

Next, at 98, it is determined whether the objective function is tooptimize profit. For this example, the answer is yes. Hence, at 100,information is generated on the cost functions that exist within thecasino operation that are impacted by the decision variables. Otherwise,if the answer were no, then, the procedure would resume at 116 of FIGS.5A-5B. This path is discussed below in connection with Example 8.

The relevant gaming departments to be analyzed are determined at 100.For this example, those include the table games department 18 and theEGD department 16 of FIG. 1, although casino management could alsochoose to incorporate the cost functions associated with otherdepartments (not shown) that are related to other gaming units, such as14, and other gaming activities (e.g., casino cage; soft count). Then,at 102, time series data including total expenses incurred by the tablegames department 18 and the EGD department 16 along with correspondingnumber of total gaming units on the casino floor 6, respectively, foreach of the example 36 months is retrieved from the FRS 4. At 104, adistinction is made between fixed and variable costs for the table gamesdepartment 18 and the EGD department 16, and like data is aggregated, at106, in order to produce four separate time series that represent fixedtable games costs, variable table game costs, fixed EGD costs andvariable EGD costs. As fixed costs are, by definition, not a function oftotal units on the casino floor, the fixed cost functions for the EGDand table games departments 16,18 consist simply of constants, at 108.These constants, for this example, are determined as shown in Equations7 and 8:

FC _(TG)=$16,280  (Eq. 7)

wherein:FC_(TG) represents the monthly fixed costs in the table gamesdepartment.

FC _(EGD)=$21,150  (Eq. 8)

wherein:FC_(EGD) represents the monthly fixed costs in the EGD department.The remaining time series data represents variable costs for the tablegames department 10 and the EGD department 12. These time series areanalyzed, at 110, to determine the mathematical expression thatrepresents the data most accurately. The expression for variable costtable games data, in this example, is determined to be Equation 9:

VC _(TG)=$9,459*ΣDV _(TG).  (Eq. 9)

wherein:VC_(TG) represents the monthly variable costs generated from the tablegames department as a function of the total number of table game unitson the casino floor (ΣDV_(TG)).

As was discussed in greater detail, above, in connection with FIGS.5A-5B, by reviewing statistical properties (e.g., r² statistic)associated with the example linear Equation 9, it is determined, at 112,that Equation 9 represents a “best fit” equation and thus represents theproper variable cost function for the table games department (VC_(TG)).

Next, at 114, it is determined if all variable cost functions have beendetermined and the process is repeated, at 110 and 112, in order thatthe variable cost function for the EGD department is determined. In thisexample, the fixed cost constants and variable cost functions aresummarized by Table 3:

TABLE 3 Equation Cost Function 7 FC_(TG) FC_(TG) = $16,280 8 FC_(EGD)FC_(EGD) = $21,150 9 VC_(TG) VC_(TG) = $9,459 * Σ DV_(TG) 10 VC_(EGD)VC_(EGD) = $420 * Σ DV_(EGD)

Although four linear equations are shown in Table 3, it will beappreciated that a wide range of counts and of different types ofequations may be employed, such as, without limitation, polynomial,logarithmic, exponential, or power. Although monthly revenue isdisclosed, smaller or larger time periods may be employed.

Next, at 116, constraints are determined that realistically representthe state of the casino operation. In this example, the constraints aredetermined and translated into mathematical expressions, at 118. Forexample, the casino floor may be limited to 24,000 square feet.Subsequently, all decision variables are assigned a square footageamount (i.e., the area that they require to be operational). Forexample, these assignments may be: DV_(TG1): 40 sq. ft.; DV_(TG2): 100sq. ft.; DV_(TG3): 80 sq. ft.; DV_(EGD1): 20 sq. ft.; DV_(EGD2): 20 sq.ft.; and DV_(EGD3): 20 sq. ft.

In this example, the decision variable values are represented byintegers (e.g., there cannot be half of a table game; there cannot be athird of an EGD) and are non-negative.

Finally, given casino management's understanding of their target gamingpatrons, minimums and maximums may be imposed on the selection problemin order to assure that the solution is reasonable and, thus, serves theneeds of the casino patrons. These minimum and maximum counts aredefined for this example as shown in Table 4:

TABLE 4 Minimum Decision Variable Maximum 10 DV_(TG1) 20 1 DV_(TG2) 10 1DV_(TG3) 5 25 DV_(EGD1) 100 25 DV_(EGD2) 1000 25 DV_(EGD3) 500

All demand, cost and constraint expressions for this selection problemare reviewed, at 120, to determine if all expressions are linear. Thisis to determine which optimization process is employed—linearprogramming or non-linear programming. In this example, all expressionsare linear, at 122, and, thus, the procedure resumes at 124. At 124,suitable linear programming techniques are applied to solve for thevalues of the decision variables that will maximize the objectivefunction and optimize profitability given the set constraints. Thisprovides the solution shown in Table 5:

TABLE 5 Decision Variable Value DV_(TG1) 20 DV_(TG2) 10 DV_(TG3) 5DV_(EGD1) 100 DV_(EGD2) 500 DV_(EGD3) 490

Then, at 126, this solution is reviewed for reasonableness. Since thisis a workable mix of blackjack, craps, and roulette tables, and nickel,quarter and dollar slot machines that will fit within the 24,000 squarefoot casino, it is accepted and the example is done at 140.

Example 7

This example shows an unreasonable solution and the constraints beingadjusted or added at 128 of FIG. 5B. This example is similar to Example6 except that Table 6 replaces Table 4 and Table 7 replaces Table 5.

As shown in Table 6, given the initial constraints provided in Table 7,the solution at 124 of FIG. 5B suggests that the casino should employ594 blackjack tables and only one of each of the values of the otherdecision variables (e.g., craps, roulette, Nickel EGD, Quarter EGD,Dollar EGD). This solution is considered unreasonable at 126, and theconstraints are adjusted at 128 to be those shown in Table 4 of Example6, which would produce a reasonable solution at 124 and 126.

TABLE 6 Minimum Decision Variable Maximum 1 DV_(TG1) 2000 1 DV_(TG2)2000 1 DV_(TG3) 2000 1 DV_(EGD1) 2000 1 DV_(EGD2) 2000 1 DV_(EGD3) 2000

TABLE 7 Decision Variable Value DV_(TG1) 594 DV_(TG2) 1 DV_(TG3) 1DV_(EGD1) 1 DV_(EGD2) 1 DV_(EGD3) 1

Example 8

This example is similar to Example 6, except that revenue is optimizedrather than profit, Table 2 is replaced by Table 8, Table 3 is notemployed, Table 4 is replaced by Table 9, and Table 5 is replaced byTable 10:

TABLE 8 Equation Decision Variable Demand Function 1′ DV_(TG1) y =$24,000 * DV_(TG1) 2′ DV_(TG2) y = $19,980 * DV_(TG2) 3′ DV_(TG3) y =$14,340 * DV_(TG3) 4′ DV_(EGD1) y = $2,460 * DV_(EGD1) 5′ DV_(EGD2) y =$2,490 * DV_(EGD2) 6′ DV_(EGD3) y = $930 * DV_(EGD3)

TABLE 9 Minimum Decision Variable Maximum 10 DV_(TG1) 100 2 DV_(TG2) 1002 DV_(TG3) 100 25 DV_(EGD1) 100 25 DV_(EGD2) 500 25 DV_(EGD3) 500

TABLE 10 Decision Variable Value DV_(TG1) 100 DV_(TG2) 100 DV_(TG3) 100DV_(EGD1) 25 DV_(EGD2) 50 DV_(EGD3) 25

Example 9

This example is similar to Example 6, except that revenue is optimizedrather than profit, Table 2 is replaced by Table 11, Table 3 is notemployed, Table 4 is replaced by Table 12, and Table 5 is replaced byTable 13:

TABLE 11 Equation Decision Variable Demand Function 1″ DV_(TG1) y =$24,000 * DV_(TG1) 2″ DV_(TG2) y = $19,980 * DV_(TG2) 3″ DV_(TG3) y =$14,340 * DV_(TG3) 4″ DV_(EGD1) y = $2,460 * DV_(EGD1) 5″ DV_(EGD2) y =$DV_(EGD2) ² + $400 * DV_(EGD2) + $50 6″ DV_(EGD3) y = $930 * DV_(EGD3)

TABLE 12 Minimum Decision Variable Maximum 10 DV_(TG1) 100 2 DV_(TG2)100 2 DV_(TG3) 100 25 DV_(EGD1) 100 25 DV_(EGD2) 500 25 DV_(EGD3) 500

TABLE 13 Decision Variable Value DV_(TG1) 100 DV_(TG2) 88 DV_(TG3) 2DV_(EGD1) 27 DV_(EGD2) 500 DV_(EGD3) 25In this example, the demand function (Equation 5″) for EGD₂ is clearlynon-linear (i.e., contains a term with a power greater than 1).Therefore, suitable non-linear programming techniques are employed, at134, to solve for the values of the decision variables that willmaximize the objective function (optimize revenue) given the setconstraints. In this example, the values are accepted and the example isdone at 142.

Example 10

This example shows detailed single decision variable variations inhistorical and current demand. Demand functions are generated from datareadily available from the CMS 2 and FRS 4 of FIG. 1. Generally, suchdata is available on a daily basis and can often be obtained on anhourly or by-shift basis. Table 14 shows a typical partial (for economyof disclosure, only some of the data at some of the dates are shown)data series used to determine the demand function for Blackjack tables:

TABLE 14 Date No. Units (Var. X) Blackjack Revenue (Var. Y) Jan. 5, 200420 $9,320 Jan. 4, 2004 20 $9,010 Jan. 3, 2004 20 $8,885 Jan. 2, 2004 20$8,600 Jan. 1, 2004 20 $9,576 Dec. 31, 2003 20 $8,045 Dec. 30, 2003 22$11,000 . . . . . . . . . Mar. 16, 2000 28 $12,010 Mar. 15, 2000 28$11,980

Running a regression analysis on the data from Table 14 will reveal thedemand function. As this data series includes both generally currentrevenue data (e.g., yesterday; one week ago; last month) as well ashistorical data (e.g., last year; two years ago), the resultant curvewill show the effects of changes in revenue (variable Y) on changes innumber of units (variable X) over both the short and long term. In thisway, the demand function incorporates both short-term demand informationas well as historical demand information.

Examples 11-14 consider other alternative example constraints that canbe imposed in the disclosed method, at 116.

Example 11

If the decision variables are units defined by their manufacturer (e.g.,Bally Gaming System EGDs; International Gaming Technology EGDs), thenconstraints may be developed to, for example, force a solution requiringat least a certain minimum number of units for one or more manufacturersin order to take advantage of quantity purchasing discounts. Hence,there may be a plurality of different electronic gaming devices, withone of the decision functions being a minimum count of the differentelectronic gaming devices from a particular manufacturer. Theconstraint, thus, associates a discount from the particular manufacturerwith the minimum count, and ensures that the discount is received byproviding the minimum count with the optimal values, as determined.

Example 12

If the decision variables are units defined by their personality or“theme” (e.g., Double Diamond; Wheel of Fortune), then constraints maybe developed to, for example, force a solution requiring a certainmaximum number of units for each theme to ensure that there are not toomany of one type and thus provide a variety of choices available to thegaming patron. Hence, for example, by employing as one of the decisionfunctions a maximum count of the different EGDs of a particular one ofthe personalities or themes (e.g., Double Diamond), then the constraintis employed to require that maximum count when the optimal values aredetermined.

Example 13

Constraints may be developed to force the solution set to adhere to acertain market positioning (e.g., relatively more table games andrelatively less EGDs; relatively more EGDs and relatively less tablegames). Hence, the constraint may require, for example, a greater countof the EGDs with respect to the table games, when the optimal values aredetermined.

Example 14

Constraints may be developed to consider the physical space availablefor unit placement. Here, for example, the casino floor may have aphysical area, and there may be a physical area associated with each ofthe different gaming units. The constraint may require that the totalcombined physical area of the different EGDs and the total combinedphysical area of the different table games is less than or equal to thetotal physical area of the casino floor when the optimal values aredetermined.

Example 15

Although Equations 7-10 of Table 3 show total fixed costs and totalvariable costs for each of the EGD and table games departments, it ispossible to determine an historical fixed cost function and anhistorical variable cost function for each of the different classes ofgaming units. For example, the FRS 4 of FIG. 1 may provide, directly orindirectly, fixed and variable cost functions for each one of the gamingunits 8, or for each of the classes of the gaming units.

Example 16

Although the CMS 2 and FRS 4 of FIG. 1 are shown, data collection withrespect to relevant data associated with the gaming units or unitcharacteristics or classifications may originate from a wide range ofsources (e.g., manufacturers; suppliers; taxing authorities; regulatoryauthorities; industry organizations).

Example 17

Step 90 of FIG. 5A may obtain the current and historical times seriesdata for the decision variables from a user interface of a financialreporting system, such as, for example, a graphical user interface, atext user interface, a DOS user interface, or any other electronic orautomated user interface (e.g., of the CMS 2 or FMS 4 of FIG. 1),wherein a user retrieves revenue (“win”) and total unit count timeseries data by classes (e.g., decision variables).

Example 18

Similar to Example 17, step 102 of FIG. 5A may determine the time seriesdata on fixed and variable costs for relevant gaming departments (e.g.,table games department; EGD department) from a user interface of afinancial reporting system.

Example 19

As an alternative to or in addition to even steps 90-96 of FIG. 5A, ifparticipation gaming units are/were on the casino floor at any timeduring the selected analysis period of step 88, then a separate timeseries is retrieved that shows total revenue from participation games,along with the number of games on the casino floor for the same timeperiod.

Example 20

As an alternative to or in addition to even steps 100-114 of FIG. 5A, ifparticipation gaming units are/were on the casino floor at any timeduring the selected analysis period of step 88, then a separate timeseries is retrieved that shows total participation royalties (i.e.,costs to the casino) and other costs, along with the number of games onthe casino floor for the same points in time. These royalties and othercosts are segregated between fixed and variable costs at 104. A fixedcost function for participation games is determined at 108. A variablecost function for participation games is defined using the regressionanalysis techniques at step 110. Depending on the nature of the variablecost data series, the resultant best-fit equation (e.g., least squares)from step 112 may be, for example, linear, polynomial, logarithmic,exponential or power.

The disclosed linear and non-linear programming techniques for agambling enterprise as applied to particular gaming unit types andclasses is extremely advantageous to gambling operators. Thesetechniques, more specifically, permit the gambling industry to predictand optimize revenue or profit for a particular gaming unit type orclass and to determine exactly what combination of gaming unit types orclasses should be deployed on the casino floor for maximum revenue orprofit.

While for clarity of disclosure reference has been made herein to theexemplary user interface display 158 for displaying optimal counts, itwill be appreciated that such counts may be stored, printed on hardcopy, be computer modified, or be combined with other data. All suchprocessing shall be deemed to fall within the terms “display” or“displaying” as employed herein.

While specific embodiments of the invention have been described indetail, it will be appreciated by those skilled in the art that variousmodifications and alternatives to those details could be developed inlight of the overall teachings of the disclosure. Accordingly, theparticular arrangements disclosed are meant to be illustrative only andnot limiting as to the scope of the invention which is to be given thefull breadth of the claims appended and any and all equivalents thereof.

What is claimed is:
 1. A method for optimizing revenue or profit for agambling enterprise, said method comprising: employing a plurality ofdifferent gaming units in said gambling enterprise; employing aplurality of counts, one of said counts for each of said differentgaming units; employing a plurality of decision functions, at least oneof said decision functions for each of said different gaming units;employing revenue or profit optimization as an objective function;determining with a processor optimal values for said counts for each ofsaid different gaming units from said decision functions in order tooptimize said objective function; and adjusting said counts for each ofsaid different gaming units to be said optimal values for said counts insaid gambling enterprise.
 2. The method of claim 1 further comprising:employing revenue optimization as said objective function; employing atleast one constraint for at least some of said different gaming units asone of said decision functions; determining time series revenue data forsaid different gaming units and determining a plurality of demandfunctions, one of said demand functions for each of said differentgaming units; employing said demand functions as some of said decisionfunctions; and determining said optimal values from said at least oneconstraint and said demand functions.
 3. A method for optimizing revenuefor a gambling enterprise, said method comprising: identifying aplurality of different classes of gaming units in said gamblingenterprise; employing a plurality of counts, one of said counts for eachof said different classes; employing at least one decision function forsaid different classes; employing revenue optimization as an objectivefunction; determining time series revenue data for said differentclasses and determining a plurality of demand functions, one of saiddemand functions for each of said different classes; determining with aprocessor optimal values for said counts for each of said differentclasses from said at least one decision function and said demandfunctions, in order to optimize said objective function and optimizerevenue from said different classes of gaming units; and adjusting saidcounts for each of said different classes to be said optimal values forsaid counts in said gambling enterprise.
 4. The method of claim 3further comprising: employing a predetermined time period of greaterthan one year for said time series revenue data and said demandfunctions.
 5. The method of claim 3 further comprising: employing one oflinear programming and non-linear programming to determine said optimalvalues for said counts.
 6. The method of claim 3 further comprising:employing a plurality of constraints associated with said differentclasses of gaming units; translating said constraints to a plurality ofmathematical expressions; employing said mathematical expressions assaid at least one decision function; and employing said demand functionsand said mathematical expressions to determine said optimal values forsaid counts.
 7. The method of claim 6 further comprising: employing oneof linear programming and non-linear programming to determine saidoptimal values for said counts.
 8. The method of claim 6 furthercomprising: determining whether said optimal values for said counts arereasonable values based upon said optimal values for said counts foreach of said different classes resulting in a floor space of thedifferent classes of the gaming units in said gambling enterprise beingless than a predetermined value; responsively adjusting saidconstraints; translating said adjusted constraints to a plurality ofcorresponding mathematical expressions; and re-determining optimalvalues for said counts for each of said different classes from saiddemand functions and said corresponding mathematical expressions, inorder to optimize said objective function and optimize revenue from saiddifferent classes of gaming units.
 9. The method of claim 6 furthercomprising: employing as one of said constraints a minimum count and amaximum count of a corresponding one of said different classes of gamingunits.
 10. The method of claim 3 further comprising: employing as saiddifferent classes of gaming units in said gambling enterprise aplurality of different classes of table games and a plurality ofdifferent classes of electronic gaming devices.
 11. The method of claim10 further comprising: differentiating said different classes ofelectronic gaming devices by denomination of wager; and differentiatingsaid different classes of table games by type of game.
 12. The method ofclaim 3 further comprising: determining said time series revenue datafrom a user interface of a financial reporting system.
 13. The method ofclaim 3 further comprising: employing regression analysis to converteach of said time series revenue data for said different classes to amathematical expression for a corresponding one of said demandfunctions.
 14. The method of claim 13 further comprising: for one ofsaid time series revenue data for a corresponding one of said differentclasses, employing as said mathematical expression for a correspondingone of said demand functions a first type of mathematical expression;selecting a different second type of mathematical expression; employingregression analysis to convert said one of said time series revenue datafor the corresponding one of said different classes to said differentsecond type of mathematical expression for the corresponding one of saiddemand functions; and employing one of said first type of mathematicalexpression and said second type of mathematical expression whichprovides a better fit of said one of said time series revenue data. 15.The method of claim 13 further comprising: analyzing said mathematicalexpression for each of said demand functions and selecting one of linearprogramming and non-linear programming to determine said optimal valuesfor said counts for each of said different classes.
 16. The method ofclaim 3 further comprising: employing a predetermined time period and apredetermined count of samples for said time series revenue data andsaid demand functions; and including with said time series revenue dataa revenue value and a count of said different classes of gaming unitsfor each of said samples.
 17. The method of claim 3 further comprising:employing at least one constraint associated with said different classesof gaming units; translating said at least one constraint to at leastone mathematical expression; employing said at least one mathematicalexpression as said at least one decision function; translating saiddemand functions to a plurality of polynomial equations; and determiningsaid optimal values from said at least one mathematical expression andsaid polynomial equations, in order to optimize said objective function.18. The method of claim 3 further comprising: displaying said optimalvalues.